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Efficient Natural Language Response Suggestion for Smart Reply
Matthew L. Henderson,Rami Al-Rfou,Brian Strope,Yun-Hsuan Sung,László Lukács,Ruiqi Guo,Sanjiv Kumar,Balint Miklos,Ray Kurzweil +8 more
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TLDR
A computationally efficient machine-learned method for natural language response suggestion using feed-forward neural networks using n-gram embedding features that achieves the same quality at a small fraction of the computational requirements and latency.Abstract:
This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode messages into vectors which are optimized to give message-response pairs a high dot-product value. An optimized search finds response suggestions. The method is evaluated in a large-scale commercial e-mail application, Inbox by Gmail. Compared to a sequence-to-sequence approach, the new system achieves the same quality at a small fraction of the computational requirements and latency.read more
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Proceedings ArticleDOI
Retrieval-guided Dialogue Response Generation via a Matching-to-Generation Framework
TL;DR: A novel framework in which the skeleton extraction is made by an interpretable matching model and the following skeleton-guided response generation is accomplished by a separately trained generator is presented.
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Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Han Zhang,Songlin Wang,Kang Zhang,Zhiling Tang,Yunjiang Jiang,Yun Xiao,Weipeng Yan,Wen-Yun Yang +7 more
TL;DR: In this paper, a Deep Personalized and Semantic Retrieval (DPRR) model is proposed to retrieve items that are semantically relevant but not exact matching to query terms.
Proceedings ArticleDOI
SolutionChat: Real-time Moderator Support for Chat-based Structured Discussion
TL;DR: SolutionChat is introduced, a system that visualizes discussion stages and featured opinions and recommends contextually appropriate moderator messages that envision untrained moderators to effectively facilitate chat-based discussions of important community matters.
Journal ArticleDOI
Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study.
TL;DR: Zhang et al. as discussed by the authors studied the extent of polarization and examined the structure of echo chambers related to COVID-19 discourse on Twitter in the United States, and found that most of the highly influential users were partisan, which may contribute to further polarization.
Proceedings ArticleDOI
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
Harish Tayyar Madabushi,Edward Gow-Smith,Marcos Garcia,Carolina Scarton,Marco Idiart,Aline Villavicencio +5 more
TL;DR: The shared task on Multilingual Idiomaticity Detection and Sentence Embedding is presented, which consists of a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
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Proceedings ArticleDOI
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